opencv/modules/imgproc/test/test_thresh.cpp

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#include "test_precomp.hpp"

namespace opencv_test { namespace {

BIGDATA_TEST(Imgproc_Threshold, huge)
{
    Mat m(65000, 40000, CV_8U);
    ASSERT_FALSE(m.isContinuous());

    uint64 i, n = (uint64)m.rows*m.cols;
    for( i = 0; i < n; i++ )
        m.data[i] = (uchar)(i & 255);

    cv::threshold(m, m, 127, 255, cv::THRESH_BINARY);
    int nz = cv::countNonZero(m);  // FIXIT 'int' is not enough here (overflow is possible with other inputs)
    ASSERT_EQ((uint64)nz, n / 2);
}

TEST(Imgproc_Threshold, threshold_dryrun)
{
    Size sz(16, 16);
    Mat input_original(sz, CV_8U, Scalar::all(2));
    Mat input = input_original.clone();
    std::vector<int> threshTypes = {THRESH_BINARY, THRESH_BINARY_INV, THRESH_TRUNC, THRESH_TOZERO, THRESH_TOZERO_INV};
    std::vector<int> threshFlags = {0, THRESH_OTSU, THRESH_TRIANGLE};
    for(int threshType : threshTypes)
    {
        for(int threshFlag : threshFlags)
        {
            const int _threshType = threshType | threshFlag | THRESH_DRYRUN;
            cv::threshold(input, input, 2.0, 0.0, _threshType);
            EXPECT_MAT_NEAR(input, input_original, 0);
        }
    }
}

typedef tuple < bool, int, int, int, int > Imgproc_Threshold_Masked_Params_t;

typedef testing::TestWithParam< Imgproc_Threshold_Masked_Params_t > Imgproc_Threshold_Masked_Fixed;

TEST_P(Imgproc_Threshold_Masked_Fixed, threshold_mask_fixed)
{
    bool useROI = get<0>(GetParam());
    int depth = get<1>(GetParam());
    int cn = get<2>(GetParam());
    int threshType = get<3>(GetParam());
    int threshFlag = get<4>(GetParam());

    const int _threshType = threshType | threshFlag;
    Size sz(127, 127);
    Size wrapperSize = useROI ? Size(sz.width+4, sz.height+4) : sz;
    Mat wrapper(wrapperSize, CV_MAKETYPE(depth, cn));
    Mat input = useROI ? Mat(wrapper, Rect(Point(), sz)) : wrapper;
    cv::randu(input, cv::Scalar::all(0), cv::Scalar::all(255));

    Mat mask = cv::Mat::zeros(sz, CV_8UC1);
    cv::RotatedRect ellipseRect((cv::Point2f)cv::Point(sz.width/2, sz.height/2), (cv::Size2f)sz, 0);
    cv::ellipse(mask, ellipseRect, cv::Scalar::all(255), cv::FILLED);//for very different mask alignments

    Mat output_with_mask = cv::Mat::zeros(sz, input.type());
    cv::thresholdWithMask(input, output_with_mask, mask, 127, 255, _threshType);

    cv::bitwise_not(mask, mask);
    input.copyTo(output_with_mask, mask);

    Mat output_without_mask;
    cv::threshold(input, output_without_mask, 127, 255, _threshType);
    input.copyTo(output_without_mask, mask);

    EXPECT_MAT_NEAR(output_with_mask, output_without_mask, 0);
}

INSTANTIATE_TEST_CASE_P(/*nothing*/, Imgproc_Threshold_Masked_Fixed,
    testing::Combine(
        testing::Values(false, true),//use roi
        testing::Values(CV_8U, CV_16U, CV_16S, CV_32F, CV_64F),//depth
        testing::Values(1, 3),//channels
        testing::Values(THRESH_BINARY, THRESH_BINARY_INV, THRESH_TRUNC, THRESH_TOZERO, THRESH_TOZERO_INV),// threshTypes
        testing::Values(0)
    )
);

typedef testing::TestWithParam< Imgproc_Threshold_Masked_Params_t > Imgproc_Threshold_Masked_Auto;

TEST_P(Imgproc_Threshold_Masked_Auto, threshold_mask_auto)
{
    bool useROI = get<0>(GetParam());
    int depth = get<1>(GetParam());
    int cn = get<2>(GetParam());
    int threshType = get<3>(GetParam());
    int threshFlag = get<4>(GetParam());

    if (threshFlag == THRESH_TRIANGLE && depth != CV_8U)
        throw SkipTestException("THRESH_TRIANGLE option supports CV_8UC1 input only");

    const int _threshType = threshType | threshFlag;
    Size sz(127, 127);
    Size wrapperSize = useROI ? Size(sz.width+4, sz.height+4) : sz;
    Mat wrapper(wrapperSize, CV_MAKETYPE(depth, cn));
    Mat input = useROI ? Mat(wrapper, Rect(Point(), sz)) : wrapper;
    cv::randu(input, cv::Scalar::all(0), cv::Scalar::all(255));

    //for OTSU and TRIANGLE, we use a rectangular mask that can be just cropped
    //in order to compute the threshold of the non-masked version
    Mat mask = cv::Mat::zeros(sz, CV_8UC1);
    cv::Rect roiRect(sz.width/4, sz.height/4, sz.width/2, sz.height/2);
    cv::rectangle(mask, roiRect, cv::Scalar::all(255), cv::FILLED);

    Mat output_with_mask = cv::Mat::zeros(sz, input.type());
    const double autoThreshWithMask = cv::thresholdWithMask(input, output_with_mask, mask, 127, 255, _threshType);
    output_with_mask = Mat(output_with_mask, roiRect);

    Mat output_without_mask;
    const double autoThresholdWithoutMask = cv::threshold(Mat(input, roiRect), output_without_mask, 127, 255, _threshType);

    ASSERT_EQ(autoThreshWithMask, autoThresholdWithoutMask);
    EXPECT_MAT_NEAR(output_with_mask, output_without_mask, 0);
}

INSTANTIATE_TEST_CASE_P(/*nothing*/, Imgproc_Threshold_Masked_Auto,
    testing::Combine(
        testing::Values(false, true),//use roi
        testing::Values(CV_8U, CV_16U),//depth
        testing::Values(1),//channels
        testing::Values(THRESH_BINARY, THRESH_BINARY_INV, THRESH_TRUNC, THRESH_TOZERO, THRESH_TOZERO_INV),// threshTypes
        testing::Values(THRESH_OTSU, THRESH_TRIANGLE)
    )
);

TEST(Imgproc_Threshold, regression_THRESH_TOZERO_IPP_16085)
{
    Size sz(16, 16);
    Mat input(sz, CV_32F, Scalar::all(2));
    Mat result;
    cv::threshold(input, result, 2.0, 0.0, THRESH_TOZERO);
    EXPECT_EQ(0, cv::norm(result, NORM_INF));
}

TEST(Imgproc_Threshold, regression_THRESH_TOZERO_IPP_21258)
{
    Size sz(16, 16);
    float val = nextafterf(16.0f, 0.0f);  // 0x417fffff, all bits in mantissa are 1
    Mat input(sz, CV_32F, Scalar::all(val));
    Mat result;
    cv::threshold(input, result, val, 0.0, THRESH_TOZERO);
    EXPECT_EQ(0, cv::norm(result, NORM_INF));
}

TEST(Imgproc_Threshold, regression_THRESH_TOZERO_IPP_21258_Min)
{
    Size sz(16, 16);
    float min_val = -std::numeric_limits<float>::max();
    Mat input(sz, CV_32F, Scalar::all(min_val));
    Mat result;
    cv::threshold(input, result, min_val, 0.0, THRESH_TOZERO);
    EXPECT_EQ(0, cv::norm(result, NORM_INF));
}

TEST(Imgproc_Threshold, regression_THRESH_TOZERO_IPP_21258_Max)
{
    Size sz(16, 16);
    float max_val = std::numeric_limits<float>::max();
    Mat input(sz, CV_32F, Scalar::all(max_val));
    Mat result;
    cv::threshold(input, result, max_val, 0.0, THRESH_TOZERO);
    EXPECT_EQ(0, cv::norm(result, NORM_INF));
}

TEST(Imgproc_AdaptiveThreshold, mean)
{
    const string input_path = cvtest::findDataFile("../cv/shared/baboon.png");
    Mat input = imread(input_path, IMREAD_GRAYSCALE);
    Mat result;

    cv::adaptiveThreshold(input, result, 255, ADAPTIVE_THRESH_MEAN_C, THRESH_BINARY, 15, 8);

    const string gt_path = cvtest::findDataFile("../cv/imgproc/adaptive_threshold1.png");
    Mat gt = imread(gt_path, IMREAD_GRAYSCALE);
    EXPECT_EQ(0, cv::norm(result, gt, NORM_INF));
}

TEST(Imgproc_AdaptiveThreshold, mean_inv)
{
    const string input_path = cvtest::findDataFile("../cv/shared/baboon.png");
    Mat input = imread(input_path, IMREAD_GRAYSCALE);
    Mat result;

    cv::adaptiveThreshold(input, result, 255, ADAPTIVE_THRESH_MEAN_C, THRESH_BINARY_INV, 15, 8);

    const string gt_path = cvtest::findDataFile("../cv/imgproc/adaptive_threshold1.png");
    Mat gt = imread(gt_path, IMREAD_GRAYSCALE);
    gt = Mat(gt.rows, gt.cols, CV_8UC1, cv::Scalar(255)) - gt;
    EXPECT_EQ(0, cv::norm(result, gt, NORM_INF));
}

TEST(Imgproc_AdaptiveThreshold, gauss)
{
    const string input_path = cvtest::findDataFile("../cv/shared/baboon.png");
    Mat input = imread(input_path, IMREAD_GRAYSCALE);
    Mat result;

    cv::adaptiveThreshold(input, result, 200, ADAPTIVE_THRESH_GAUSSIAN_C, THRESH_BINARY, 21, -5);

    const string gt_path = cvtest::findDataFile("../cv/imgproc/adaptive_threshold2.png");
    Mat gt = imread(gt_path, IMREAD_GRAYSCALE);
    EXPECT_EQ(0, cv::norm(result, gt, NORM_INF));
}

TEST(Imgproc_AdaptiveThreshold, gauss_inv)
{
    const string input_path = cvtest::findDataFile("../cv/shared/baboon.png");
    Mat input = imread(input_path, IMREAD_GRAYSCALE);
    Mat result;

    cv::adaptiveThreshold(input, result, 200, ADAPTIVE_THRESH_GAUSSIAN_C, THRESH_BINARY_INV, 21, -5);

    const string gt_path = cvtest::findDataFile("../cv/imgproc/adaptive_threshold2.png");
    Mat gt = imread(gt_path, IMREAD_GRAYSCALE);
    gt = Mat(gt.rows, gt.cols, CV_8UC1, cv::Scalar(200)) - gt;
    EXPECT_EQ(0, cv::norm(result, gt, NORM_INF));
}

}} // namespace